Introduction

This project uses the Global Visa Cost Dataset (Recchi et al., 2020) created by researchers from the European University Institute's Migration Policy Centre, which provides detailed information on visa costs between country pairs for seven categories in 2019 (see Appendix for further explanation). Data was primarily collected manually from governmental websites. Three types of interactive graphs – Sankey diagram, bar chart, and choropleth – will be employed to visualise data in the following sections.

import pandas as pd  # to deal with DataFrames

# Defining path to the dataset file and reading in the dataset
path = "E:/Jupyter Notebooks/COMM327/Portfolio2/GMP_GlobalVisaCostDataset_version1.1.csv"
df = pd.read_csv(path, delimiter='\t')  # read in dataset via pd.read_csv() function

#【delimiter】
# This parameter determines the separator used to interpret the in CSV file, where '\t' denotes 
# a tab-separated value (Pandas, 2024e).

Data Visualisation and Analysis

Sankey Diagram

Cleaning DataFrame

# Defining a new variable df0 to store DataFrame without missing data in the specified columns, 
# to ensure that the sankey diagram will only contain valid nodes.

df0 = df.dropna(
    subset=[
        'tourist_visa',
        'source',
        'source_region',
        'source_subregion',
    ]
)

#【.dropna()】
# This method drops rows with any missing data (in the specified columns) (Pandas, 2024b).

#【subset】
# This parameter is used to specify which columns to consider when removing rows with missing data. 
# In this case, the columns will be used to create a sankey diagram.

# For convenience, this project will only use data about tourist visa, as the most common type of 
# visa (Recchi et al., 2021, p. 7).

Defining Nodes

# Defining lists to store labels for nodes
source_region_labels = list(df0['source_region'].unique())
source_subregion_labels = list(df0['source_subregion'].unique())

#【.unique()】
# This method returns an Array object containing a list of unique values from a Series object 
# (Pandas, 2024f). In this case, the columns 'source_region' and 'source_subregion' in the 
# DataFrame df0 serve as the Series objects, where missing values are already dropped via 
# .dropna() method. Subsequently, .unique() method is used to return Array objects containing 
# lists of unique values from each column. These values will be used to create node labels. 

#【list()】
# This built-in Python function (also class) converts the input to a list (Python, 2024a). 
# In this case, it is used to convert the Array objects mentioned above into Python list objects.

# Combining lists to define an entire list storing all node labels
node_labels = ['World'] + source_region_labels + source_subregion_labels

#【+】
# This operator concatenates two or more sequences (e.g., lists) into a single sequence 
# (Python, 2024b). In this case, it is used to combine lists of node labels into a single list.

#【'World'】
# 'World' is added to the start of the list, serving as the root node label.

# Defining an an iterable range object to store the index for each node
node_ids = range(len(node_labels))

#【len()】
# This built-in Python function returns the number of items in an object (i.e., the length of an object), 
# which can be a sequence (e.g., list) or a collection (e.g., dictionary) (Python, 2024a). In this case, 
# it is used to return the length of the list node_labels storing all node labels, which equals to 23.

#【range()】
# This built-in Python function (also class) returns a sequence of numbers, starting from zero by 
# default (Python, 2024a). In this case, range(23) starts from 0 to 22, which equal to the indices of 
# items in the list node_labels.

# Defining a dictionary to store the label and index for each node
nodes = dict(zip(node_labels, node_ids))

#【zip()】
# This built-in Python function iterate over several iterables in parallel, producing tuples with an 
# item from each one (Python, 2024a).

#【dict()】
# This built-in Python function (also class) creates a dictionary from two or more sequences 
# (Python, 2024a). In this case, it is used to create a dictionary where the keys are node labels 
# and values are the indices of node labels.

Defining Links

# Creating an aggregated DataFrame to store relevant data, which will be used to calculate the value 
# of link towards each subregion.
to_subregion_value = df0.groupby('source_subregion').agg(
    {
    'source_region':'first', # the region of each subregion 
    'tourist_visa':'mean', # mean of tourist visa cost in each subregion
    }
)

#【.groupby()】
# This method is used to group rows of df0 based on the column 'source_region'.

#【.agg()】
# This method is used to calculate the aggregation of values in each group.

#【'first'】
# This aggregation function returns the first entry of each column within each group in the 
# DataFrameGroupBy object (Pandas, 2024a). In this case, it is used to select the first region 
# encountered in each group of subregions (i.e., the region for each subregion).

# Creating an aggregated DataFrame to store the value of link towards each region.
to_region_value = to_subregion_value.groupby('source_region').agg({'tourist_visa':'sum'})

# Creating empty lists to store the source, source, and value for each link
sources = [] # to be filled with the indices of node labels of source nodes
targets = [] # to be filled with the indices of node labels of target nodes
values = [] # to be filled with the values for each link (tourist_visa)

# from 'World' to regions
for region in source_region_labels:
    # Append 'World' as the source nodes
    sources.append(nodes['World'])
    # Append each region as the target nodes
    targets.append(nodes[region])
    # Append the values in df0_agg1 for each region as the value for each link
    values.append(to_region_value.loc[region,'tourist_visa'])

# from regions to subregions
for subregion in source_subregion_labels:
    # Append each corresponding region as the source nodes
    region = to_subregion_value.loc[subregion,'source_region'] # get the corresponding region
    sources.append(nodes[region])
    # Append each subregion as the target nodes
    targets.append(nodes[subregion]) 
    # Append the values in df0_agg2 for each subregion as the value for each link
    values.append(to_subregion_value.loc[subregion,'tourist_visa'])

Assigning Colours

source_region_labels # Checking regions in df0

['Asia', 'Africa', 'Europe', 'Americas', 'Oceania']

# Defining a dictionary to store region colours

region_colours = {
    'Africa': '#6B3A09',  # Brown
    'Asia': '#7FA709',  # Green
    'Americas': '#EC6C92', # Pink
    'Oceania': '#F5DA29',  # Yellow
    'Europe': '#85D6D5',  # Blue
}

# These colours are generated (in hex) via Nichols's (no date) colour palette generator for 
# better visibility to colour blind people.

# Defining node colours based on regions

node_colours = []  # Creating an empty list to store node colours

# Using for loop to append node colours to the list
for label in node_labels:
    if label in source_region_labels:
        node_colours.append(region_colours[label])
    
    elif label in source_subregion_labels:
        region = to_subregion_value.loc[label,'source_region'] # get the corresponding region
        node_colours.append(region_colours[region])

    else: # if label is 'World'  
        node_colours.append('silver') 

import matplotlib.colors as c  # to create faint colours in RGBA format

# Defining strings to store faint colours for links in RGBA format

faint_Africa = 'rgba'+str(c.to_rgba(region_colours['Africa'], 0.5)) 
faint_Asia = 'rgba'+str(c.to_rgba(region_colours['Asia'], 0.5)) 
faint_Americas = 'rgba'+str(c.to_rgba(region_colours['Americas'], 0.5)) 
faint_Oceania = 'rgba'+str(c.to_rgba(region_colours['Oceania'], 0.5)) 
faint_Europe = 'rgba'+str(c.to_rgba(region_colours['Europe'], 0.5))

# 0.5 is the alpha value for colours, meaning 50% transparency 

# Defining a dictionary to store faint link colours based on regions

faint_region_colours = {
    'Africa': faint_Africa,  # faint brown
    'Asia': faint_Asia,  # faint green
    'Americas': faint_Americas,  # faint pink
    'Oceania': faint_Oceania,  # faint yellow
    'Europe': faint_Europe,  # faint blue
}

# Defining link colours based on regions

link_colours = []  # Creating an empty list to store link colours

# Using for loop to append link colours to the list
for source, target in zip(sources, targets):
    if node_labels[source] in source_region_labels:
        link_colours.append(faint_region_colours[node_labels[source]])
    
    elif node_labels[target] in source_region_labels:
        link_colours.append(faint_region_colours[node_labels[target]])

# Check if the number of node colours matches the number of nodes
print(len(node_colours),len(nodes))

23 23

# Check if the number of link colours matches the number of links
print(len(link_colours),len(sources),len(targets),len(values))

22 22 22 22

Creating Sankey Diagram

import plotly.graph_objects as go  # to create sankey diagram

# Creating a Sankey object to store sankey data via go.Sankey() function

sankey_data = go.Sankey(   # defining a variable to store sankey data
  # defining nodes
    node = dict(
        label = node_labels,  # labels for nodes
        color = node_colours,  # colours for nodes
        pad = 30,  # the horizontal distance between nodes
        thickness = 20,  # thickness of nodes
        line = {'color':'black','width':0.5},  # style for node outlines
    ),

  # defining links
    link = dict(
        source = sources,  # indices of source nodes in node_labels
        target = targets,  # indices of target nodes in node_labels
        value = values,  # values for links
        color = link_colours, # colours for links
    )
)

# Creating a Figure object to store sankey data via go.Figure() function
fig1 = go.Figure(sankey_data)

# Updating layout of the figure
fig1.update_layout(
    width=800, height=600,  # size of the figure
    title_text="Figure 1. Sankey diagram of average tourist visa costs from each area",
    title_x=0.5, title_y=0.05,  # location of the title
    font={'style':'italic'},  # font style of the figure
) 
    #【.update_layout()】
    # This method is used to update the layout of the figure, including the size, title, 
    # font, etc. (Plotly, 2024b).

fig1.show(renderer='notebook')  # display the sankey diagram

    #【renderer】
    # This parameter is used to specify the renderer used to render the figure (Plotly, 2024b), 
    # and 'notebook' refers to Jupyter notebook.

    # This project is edited and exported to HTML file using Visual Studio Code with Jupyter extensions, 
    # in which case renderer='notebook' is effective for diagram display within Visual Studio Code and 
    # the exported HTML file. However, this argument seems not working for diagram display within output 
    # cells using Jupyter (but working for exported HTML file).

As shown above, Figure 1 illustrates the distribution of average tourist visa costs from global regions (e.g., Asia, Europe, Africa) to their corresponding subregions (e.g., Eastern Asia, Northern Europe). The width of the links from regions to subregions represents the average tourist visa cost per country (as countries of origin) in each subregion, while the widths of the links on the left-hand side (from the ‘World’ node to each region node) are the sum of their corresponding links towards the subregions. Overall, this Sankey diagram (Figure 1) shows the flow of global tourist visa costs.

Moreover, Figure 2 provides the same Sankey diagram with manually sorted subregion nodes by their links’ values (average tourist visa costs), which could be of better interpretation for gaps between areas in tourist visa costs. As shown in Figure 2, subregions belonging to Asia and Africa exhibit significantly larger flows, indicating their higher overall tourist visa cost. In contrast, smaller flows can be seen in Europe, Australia and New Zealand, and Northern America, reflecting their lower average tourist visa costs. Meanwhile, within Asia, the Southern Asia area has the highest figure whereas Eastern Asia has the lowest. Likewise, Western Europe has the lowest figure among all European areas.

In general, Figures 1 and 2 reveal potential inequality between countries in different areas regarding the ability to leave their country. This gap will be further demonstrated in the following sections on the bar chart (Figures 3 and 4).

Bar Chart

Aggregating Data

import plotly.express as px  # to create plotly interactive graphs

# Aggregating data in order to draw the bar chart

bar_agg = df.groupby('source_subregion').agg(
    {
    'source_region':'first', # the region of each subregion
    'tourist_visa':'mean', # the mean of tourist visa cost
    'tourist_perdailyincome':'mean', # the mean of tourist visa cost per daily income
    }
).reset_index()  # resetting index to make 'source_subregion' a regular column

#【.reset_index()】
# This method is used to reset the index of the aggregated DataFrame, placing all columns into a single
# level index (Pandas, 2024c). Namely, transform the automatically generated index ('source_subregion') 
# into a regular column.

Creating Bar Chart

# Creating a bar chart to show average tourist visa cost in each subregion

fig2 = px.bar(
    bar_agg,x='tourist_visa', y='source_subregion', # specifying x and y axis
    labels={  # assigning x and y labels 
        'tourist_visa':'Average tourist visa cost ($)',  
        'source_subregion':'Subregion',
    },
    color='source_region', # grouping bars by region in different colours
    color_discrete_map = region_colours # assigning colours (previously defined)
)

# Updating layout properties of the figure
fig2.update_layout(
    width=800, height=480,  # size of the figure
    title_text='Figure 3. Bar chart of average tourist visa costs from each subregion',
    title_x=0.5, title_y=0.02,  # location of the title
    titlefont={'style':'italic'},  # font style of the title
    yaxis={'categoryorder':'total ascending'}, # sorting bars by value ascending
    legend={'title':'Region'},  # assigning legend title
) 

fig2.show(renderer='notebook') # displaying the figure

Figure 3 presents the same data as in Figure 1 and Figure 2, which is the average tourist visa costs from each area (only subregions in Figure 3), grouped by region in different colours, while the bar chart (Figure 3) can show the difference in value more clearly as compared to Figure 2 since the length in Figure 3 (bar chart) are more distinguishable than the width of links in Figure 1, which is also in line with Wilke’s (Wilke, 2019, chap. 6) suggestion that bar chart could be more suitable for presenting variations in amount of value. It may be inferred from Figures 1, 2 and 3, that people in countries in well-developed areas (e.g., Western Europe) have to pay less to go abroad whereas those in underdeveloped countries in areas such as Southern Asia and Northern Africa should pay more to go abroad. Nevertheless, another variable ‘tourist_perdailyincome’ (see Appendix for detailed description) is introduced to calculate the underlying actual data on the level of tourist visa costs when adjusted by the average daily income in each country (Recchi et al., 2021, p. 4).

# Creating a bar chart to show average tourist visa cost per daily income in each subregion, 
# namely, the average working days to afford a tourist visa from countries in each subregion.

fig3 = px.bar(
    bar_agg, x='tourist_perdailyincome', y='source_subregion',
    labels={ # assigning x and y labels
        'tourist_perdailyincome':'Average working days to afford tourist visa (day)', 
        'source_subregion':'Subregion',
    },
    color='source_region', # grouping by region in different colours
    color_discrete_map = region_colours # assigning colours (previously defined)
)

# Updating layout properties of the figure
fig3.update_layout(
    width=800, height=480,  # size of the figure
    title_text='Figure 4. Bar chart of average working days to afford tourist visa from each subregion',
    title_x=0.5, title_y=0.02,  # location of the title
    titlefont={'style':'italic'},  # font style of the title
    yaxis={'categoryorder':'total ascending'}, # sorting bars by value ascending
    legend={'title':'Region'},  # assigning legend title
) 

fig3.show(renderer='notebook') # displaying the figure

As shown in Figure 4, there are even more significant gaps between areas regarding their income-adjusted average tourist visa costs (i.e., average working days to afford a tourist visa), revealing a paradoxical global situation – the poorer a country, the more citizens in it should pay for tourist visas to go abroad.

Choropleth

Aggregating Data

# Aggregating data in order to draw the choropleth
choro_agg = (
    df.groupby('source')
    .agg({
        'source_iso3': 'first',
        'tourist_visa': 'mean',
    })
    .sort_values('tourist_visa', ascending=False)
    .reset_index()
)

#【.sort_values()】
# This method is used to sort the aggregated DataFrame by 'tourist_visa' column, in descending order 
# (ascending=False) (Pandas, 2024d).

#【.reset_index()】
# resetting index to make 'source_iso3' a regular column

Creating Choropleth

# Creating a choropleth map to show average tourist visa cost in each country
fig4 = px.choropleth(
    choro_agg, locations='source_iso3', # assign data in 'source_iso3' column to the map
    locationmode='ISO-3', # use ISO-3 codes as coding for countries
    color='tourist_visa', # vary colour of the country based on 'tourist_visa'
    hover_name='source', # content to be displayed when hovering over a country
    hover_data='tourist_visa', # data to be displayed when hovering over a country
    color_continuous_scale='aggrnyl', # color scale for the choropleth
)

# Updating layout properties of the figure
fig4.update_layout(
    title='Figure 5. Choropleth map of average tourist visa costs',
    title_x=0.5, title_y=0.02,  # location of the title
    title_font={'style':'italic'},  # font style of the title
    width=800, height=500,  # size of the figure
    coloraxis = dict(
        colorbar = dict(  # specify colorbar properties
            orientation='h',  # orientation of the colorbar: horizontal
            title='Average tourist visa cost ($)', # title of the colorbar
        )
    )
)

fig4.show(renderer='notebook') # displaying the figure

Furthermore, we may gain insights by moving from the sub-region level to the country level. Figure 5 provides information about the distribution of average tourist visa costs in specific countries around the world, which varies with the continuous colour indicator. According to Wilke (2019, p. 173), as for choropleth, it is typically accepted that dark-coloured background areas in the maps behave better in visibility than light-coloured background areas for webpages. Figure 5 thus employs a colour map with dark-coloured background areas, where the colour gradually fades with the value increasing (Plotly, 2024a).

Reflection and Possible Improvements

There is still space for improvement in this project. For instance, it only discussed one category of visa – tourist visa. Other types of visas could be included to gain more insights into nuanced variation. In addition, the colour bar of the choropleth (Figure 5) may be refined by binned continuous colour scales, because continuous colour scales seem not easy to recognise specific colour values and match them with areas in the map (Wilke, 2019, pp. 174–175).

Conclusion

In conclusion, this project visualises and analyses data about global tourist visa costs in different areas, revealing the paradoxical situation that the richer a country, the less tourist fee nationals in it need to pay to go abroad.

Bibliography

Nichols, D. (no date) Coloring for Colorblindness. Available at: https://davidmathlogic.com/colorblind/ (Accessed: 11 December 2024)

Pandas (2024a) pandas.core.groupby.DataFrameGroupBy.first. Available at: https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.first.html#pandas.core.groupby.DataFrameGroupBy.first (Accessed: 12 December 2024)

Pandas (2024b) pandas.DataFrame.dropna. Available at: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html (Accessed: 11 December 2024)

Pandas (2024c) pandas.DataFrame.reset_index. Available at: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.reset_index.html (Accessed: 11 December 2024)

Pandas (2024d) pandas.DataFrame.sort_values. Available at: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_values.html#pandas.DataFrame.sort_values (Accessed: 12 December 2024)

Pandas (2024e) pandas.read_csv. Available at: https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html (Accessed: 10 December 2024)

Pandas (2024f) pandas.Series.unique. Available at: https://pandas.pydata.org/docs/reference/api/pandas.Series.unique.html (Accessed: 10 December 2024)

Plotly (2024a) Built-in Continuous Color Scales in Python. Available at: https://plotly.com/python/builtin-colorscales/ (Accessed: 13 December 2024)

Plotly (2024b) plotly.graph_objects.Figure. Available at: https://plotly.com/python-api-reference/generated/plotly.graph_objects.Figure.html#plotly.graph_objects.Figure.show (Accessed: 10 December 2024)

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Recchi, E. et al. (2020) ‘Global Visa Cost Dataset’. Available at: https://doi.org/10.5281/zenodo.4572529

Recchi, E. et al. (2021) ‘The global visa cost divide: How and why the price for travel permits varies worldwide’, Political Geography, 86(4). Available at: https://doi.org/10.1016/j.polgeo.2021.102350

Wilke, C.O. (2019) Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. Sebastopol: O’Reilly Media, Inc.

Appendix

This appendix contains additional information about the dataset used in this project (the Global Visa Cost Dataset v1.1), including introduction of the dataset and detailed description of variables.

Global Visa Cost Dataset

The Global Visa Cost Dataset, created by researchers from the European University Institute's Migration Policy Centre, provides comprehensive data on visa costs for seven categories between country pairs in 2019. The dataset was developed through manual data collection primarily from official government websites and consulates.

Visa costs are coded in the following categories: tourist, student, business, work, family reunification, transit, and other motives, recorded in US dollars.

For countries where information was not available in English, French, Spanish, Arabic, Italian or Russian, a translation website was used to obtain the necessary information from the primary language of the country.

Several countries, including Palestine and the Syrian Arab Republic, are not coded due to a lack of information about travel access to their territory. Some countries needed a second round of visa cost checks due to difficulties in finding the information about their visa policy and visa requirements. In the case of many country pairs, there is visa policy information but there is no indication of the actual visa costs. For instance, Nauru in Oceania required a particularly engaging search to retrieve an official document on Immigration Regulations of January 2014, where the actual visa categories and costs are mentioned. This information is not indicated in any other official online source. For country pairs where information is not available in the dataset, missing values are introduced.

Variable Description

Variables: Countries and Regions

These variables represent the countries and regions of origin and destination, where 'region' refers to the continent a country is located in, and 'subregion' refers to sub-regions within continents.

source: Country of origin
target: Country of destination
source_iso3: ISO3 code of country of origin
target_iso3: ISO3 code of country of destination

source_region: World region country of origin is located in
target_region: World region country of destination is located in
source_subregion: World sub-region country of destination is located in
target_subregion: World sub-region country of destination is located in

Note: Regions and sub-regions are based on the UN M.49 GeoScheme.

Variables: Visa Costs to the Target Countries

These variables are visa costs to the target countries from the source countries based on the official visa policies and visa requirements of the target countries in 2019, measured in US dollars.

tourist_visa: Tourist visa costs (in USD)
student_visa: Student visa costs (in USD)
business_visa: Business visa costs (in USD)
work_visa: Work visa costs (in USD)
family_reunification_visa: Family reunification visa costs (in USD)
transit_visa: Transit visa costs (in USD)
other_motives_visa: Other motives visa costs (in USD)

Variables: Average Working Days to Pay Visa Costs

These variables estimates how many days people in the source countries have to work on average to be able to afford a visa to the target countries, measured in days.

tourist_perdailyincome: Tourist visa costs (in average daily incomes)
student_perdailyincome: Student visa costs (in average daily incomes)
business_perdailyincome: Business visa costs (in average daily incomes)
work_perdailyincome: Work visa costs (in average daily incomes)
family_perdailyincome: Family reunification costs (in average daily incomes)
transit_perdailyincome: Transit visa costs (in average daily incomes)
other_perdailyincome: Other motives visa costs (in average daily incomes)

Note: The authors used the GNI per capita as an approximation of the mean income of the average person in a country to calculate what the real costs of visas may be like. Information about GNI per capita are from World Bank (2019), which is available at: https://data.worldbank.org/indicator/NY.GNP.PCAP.CD .

Variable: Updates Compared to the Previous Version

This variable indicates updates to the previous version of the dataset.

updated: 1 if value was added/altered in the November 2020 update of the dataset from version 1.0 to version 1.1

Note: Updates in version 1.1 compared to version 1.0 include corrections, additional data for numerous countries, and a new variable 'updated' to track changes. This project only used the updated version (version 1.1) of the dataset.